Home About us Contact
|
Home About us Contact | ||
|
|
||
Statistical Validation (statistical + validation)
Selected AbstractsCold adaptation in the marine bacterium, Sphingopyxis alaskensis, assessed using quantitative proteomicsENVIRONMENTAL MICROBIOLOGY, Issue 10 2010Lily Ting Summary The cold marine environment constitutes a large proportion of the Earth's biosphere. Sphingopyxis alaskensis was isolated as a numerically abundant bacterium from several cold marine locations, and has been extensively studied as a model marine bacterium. Recently, a metabolic labelling platform was developed to comprehensively identify and quantify proteins from S. alaskensis. The approach incorporated data normalization and statistical validation for the purpose of generating highly confident quantitative proteomics data. Using this approach, we determined quantitative differences between cells grown at 10°C (low temperature) and 30°C (high temperature). Cold adaptation was linked to specific aspects of gene expression: a dedicated protein-folding system using GroESL, DnaK, DnaJ, GrpE, SecB, ClpB and PPIase; polyhydroxyalkanoate-associated storage materials; a link between enzymes in fatty acid metabolism and energy generation; de novo synthesis of polyunsaturated fatty acids in the membrane and cell wall; inorganic phosphate ion transport by a phosphate import PstB homologue; TonB-dependent receptor and bacterioferritin in iron homeostasis; histidine, tryptophan and proline amino acid metabolism; and a large number of proteins without annotated functions. This study provides a new level of understanding on how important marine bacteria can adapt to compete effectively in cold marine environments. This study is also a benchmark for comparative proteomic analyses with other important marine bacteria and other cold-adapted organisms. [source] Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicologyENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 8 2003Roger Perkins Abstract Quantitative structure,activity relationships (QSARs) attempt to correlate chemical structure with activity using statistical approaches. The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. Quantitative structure,activity relationships and other related approaches have attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment. An assortment of new QSAR methods have been developed during the past decade, most of them focused on drug discovery. Besides advancing our fundamental knowledge of QSARs, these scientific efforts have stimulated their application in a wider range of disciplines, such as toxicology, where QSARs have not yet gained full appreciation. In this review, we attempt to summarize the status of QSAR with emphasis on illuminating the utility and limitations of QSAR technology. We will first review two-dimensional (2D) QSAR with a discussion of the availability and appropriate selection of molecular descriptors. We will then proceed to describe three-dimensional (3D) QSAR and key issues associated with this technology, then compare the relative suitability of 2D and 3D QSAR for different applications. Given the recent technological advances in biological research for rapid identification of drug targets, we mention several examples in which QSAR approaches are employed in conjunction with improved knowledge of the structure and function of the target receptor. The review will conclude by discussing statistical validation of QSAR models, a topic that has received sparse attention in recent years despite its critical importance. [source] Mathematical improvements to maximum likelihood parallel factor analysis: theory and simulationsJOURNAL OF CHEMOMETRICS, Issue 4 2005Lorenzo Vega-Montoto Abstract A number of simplified algorithms for carrying out maximum likelihood parallel factor analysis (MLPARAFAC) for three-way data affected by different error structures are described. The MLPARAFAC method was introduced to establish the theoretical basis to treat heteroscedastic and/or correlated noise affecting trilinear data. Unfortunately, the large size of the error covariance matrix employed in the general formulation of this algorithm prevents its application to solve standard three-way problems. The algorithms developed here are based on the principle of alternating least squares, but differ from the generalized MLPARAFAC algorithm in that they do not use equivalent alternatives of the objective function to estimate the loadings for the different modes. Instead, these simplified algorithms tackle the loss of symmetry of the PARAFAC model by using only one representation of the objective function to estimate the loadings of all of the modes. In addition, a compression step is introduced to allow the use of the generalized algorithm. Simulation studies carried out under a variety of measurement error conditions were used for statistical validation of the maximum likelihood properties of the algorithms and to assess the quality of the results and computation time. The simplified MLPARAFAC methods are also shown to produce more accurate results than PARAFAC under a variety of conditions. Copyright © 2005 John Wiley & Sons, Ltd. [source] Principles of QSAR models validation: internal and externalMOLECULAR INFORMATICS, Issue 5 2007Paola Gramatica Abstract The recent REACH Policy of the European Union has led to scientists and regulators to focus their attention on establishing general validation principles for QSAR models in the context of chemical regulation (previously known as the Setubal, nowadays, the OECD principles). This paper gives a brief analysis of some principles: unambiguous algorithm, Applicability Domain (AD), and statistical validation. Some concerns related to QSAR algorithm reproducibility and an example of a fast check of the applicability domain for MLR models are presented. Common myths and misconceptions related to popular techniques for verifying internal predictivity, particularly for MLR models (for instance cross-validation, bootstrap), are commented on and compared with commonly used statistical techniques for external validation. The differences in the two validating approaches are highlighted, and evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes. [source] Corporate discourse and environmental performance in ArgentinaBUSINESS STRATEGY AND THE ENVIRONMENT, Issue 3 2008Diego A. Vazquez Abstract There is substantial research and policy interest in the relationship between firms and the natural environment, including how this relationship is influenced by regulators, international pressures, rival firms and stakeholder demands. With some exceptions, the ,softer' dimensions of environmental aspect management , how attitudes, beliefs and perceptions and the human factors drive corporate behaviour , have been understudied. The work that exists tends to be informal, and allows little scope for the statistical validation that is required for robust inference. This paper examines whether corporate values towards the environment affect a firm's environmental performance. It uses survey methods as well as content and discourse analyses of interview text and corporate reports and web sites to explore the links between managerial ,mindsets' and the environmental performance of the firms of which they are a part. Though the application is Argentina, the lessons learned can be generalized to other developing and industrialized countries. Copyright © 2006 John Wiley & Sons, Ltd and ERP Environment. [source] | |||||||||||||